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Category: information science – Page 97

A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%.

When Zoom was used for training the sound classification algorithm, the prediction accuracy dropped to 93%, which is still dangerously high, and a record for that medium.

Such an attack severely affects the target’s data security, as it could leak people’s passwords, discussions, messages, or other sensitive information to malicious third parties.

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What you get, starting out in this video, is that algorithms impact our lives in, as CSAIL grad student Sandeep Silwal puts it, “silent ways”

Silwal uses a simple example – maps – in discussing what he calls the “marriage of provable algorithm design and machine learning.”

Lots of people, he notes, want to move from the area around MIT, south across the Charles to Fenway Park, to see the Red Sox.

That sort of fact could inform the thinking about how to program algorithms. For example, Silwal mentions how you can analyze data results to identify the most visited websites on the Internet – and direct focus accordingly.

“We use (algorithms) to compute fundamental things about us,” he says. “And… More.

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Quantum entanglement is one of the most intriguing and perplexing phenomena in quantum physics. It allows physicists to create connections between particles that seem to violate our understanding of space and time.

This video discusses what quantum entanglement really is, and the experiments that help us understand it. The results of these experiments have applications in new technologies that will forever change our world.

Join Katie Mack, Perimeter Institute’s Hawking Chair in Cosmology and Science Communication, over 10 short forays into the weird, wonderful world of quantum science. Episodes are published weekly, subscribe to our channel so you don’t miss an update.

Want to learn more about quantum concepts? Visit https://perimeterinstitute.ca/quantum-101-quantum-science-explained to access free resources.

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Continue reading “Quantum 101 Episode 5: Quantum Entanglement Explained” | >

In a first-of-its-kind clinical trial, bioelectronic medicine researchers, engineers and surgeons at Northwell Health’s The Feinstein Institutes for Medical Research have successfully implanted microchips into the brain of a man living with paralysis, and have developed artificial intelligence (AI) algorithms to re-link his brain to his body and spinal cord.

This double neural bypass forms an electronic bridge that allows information to flow once again between the man’s paralyzed body and to restore movement and sensations in his hand with lasting gains in his arm and wrist outside of the laboratory. The research team unveiled the trial participant’s groundbreaking progress four months after a 15-hour open-brain surgery that took place on March 9 at North Shore University Hospital (NSUH).

“This is the first time the brain, body and have been linked together electronically in a paralyzed human to restore lasting movement and sensation,” said Chad Bouton, professor in the Institute of Bioelectronic Medicine at the Feinstein Institutes, vice president of advanced engineering at Northwell Health, developer of the technology and principal investigator of the clinical trial.

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There’s a lot of talk about the potential for artificial intelligence in medicine, but few researchers have shown through well-designed clinical trials that it could be a boon for doctors, health care providers and patients.

Now, researchers at Stanford Medicine have conducted one such trial; they tested an artificial intelligence algorithm used to evaluate heart function. The algorithm, they found, improves evaluations of heart function from echocardiograms — movies of the beating heart, filmed with ultrasound waves, that show how efficiently it pumps blood.

“This blinded, randomized clinical trial is, to our knowledge, one of the first to evaluate the performance of an artificial intelligence algorithm in medicine. We showed that AI can help improve accuracy and speed of echocardiogram readings,” said James Zou, PhD, assistant professor of biomedical data science and co-senior author on the study. “This is important because heart disease is the leading cause of death in the world. There are over 10 million echocardiograms done each year in the U.S., and AI has the potential to add precision to how they are interpreted.”

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An asteroid discovery algorithm—designed to uncover near-Earth asteroids for the Vera C. Rubin Observatory’s upcoming 10-year survey of the night sky—has identified its first “potentially hazardous” asteroid, a term for space rocks in Earth’s vicinity that scientists like to keep an eye on.

The roughly 600-foot-long asteroid, designated 2022 SF289, was discovered during a test drive of the algorithm with the ATLAS survey in Hawaii. Finding 2022 SF289, which poses no risk to Earth for the foreseeable future, confirms that the next-generation algorithm, known as HelioLinc3D, can identify near-Earth asteroids with fewer and more dispersed observations than required by today’s methods.

“By demonstrating the real-world effectiveness of the software that Rubin will use to look for thousands of yet-unknown potentially hazardous asteroids, the discovery of 2022 SF289 makes us all safer,” said Rubin scientist Ari Heinze, the principal developer of HelioLinc3D and a researcher at the University of Washington.

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Researchers from the Tokyo University of Science recently published a study in the journal Artificial Life and Robotics where they explored how machine learning can help detect deception.

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to learn and improve from experience without being explicitly programmed. In other words, it is a method of teaching computers to perform specific tasks by learning from data, patterns, and examples, rather than relying on pre-defined rules.

Detecting deception can be important in various situations, like questioning crime victims or suspects and interviewing patients with mental health issues. Sometimes, human interviewers might struggle to ask the right questions or spot deception accurately.

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Here’s a better use for AI than warfare, which coming from a military family I see as a sad but necessary thing seeing as how Russia likes to invade people lately, but I hope we can keep peace with China, but anyway I’ve always loved animals. They called me Dr Dolittle as a child because I played with animals a lot. I hope for world peace. Perhaps AI can help diplomats communicate better as well. I know, you’d think we’d be able to but it doesn’t seem to be the case.

Scientists are harnessing the power of artificial intelligence (AI) to decode animal languages.

Scientists in Israel are closer than ever to making two-way communication with another species more likely — by using AI to understand the language of bats.

“I’ve always dreamt of a Dolittle machine that will allow me to talk with animals”, said Professor Yossi Yovel of Tel Aviv University.

The team at Tel Aviv University created a large database of bat noises and videos, teaching AI how to differentiate between the different sounds. The Algorithm then matched the sounds with specific social interactions captured on film.

Continue reading “How artificial intelligence is helping scientists talk to animals — BBC News” | >

Dismantling the belief in a static universe, Edwin Hubble’s revolutionary observations in the 1920s laid the groundwork for our understanding of a continually expanding cosmos. However, we must seek to reconcile this theory with observations that are consistent with a non-expanding universe, writes Tim Anderson.

You have been taught that the universe began with a Big Bang, a hot, dense period about 13.8 billion years ago. And the reason we believe this to be true is because the universe is expanding and, therefore, was smaller in the past. The Cosmic Microwave Background is the smoking gun for the Big Bang, the result of a reionization of matter that made the universe transparent about 300–400,000 years after the Big Bang.

How did we go from Einstein modifying his equations to keep the universe static and eternal, which he called the biggest blunder of his life, to every scientist believing that the universe had a beginning in 10 years? It all started with astronomer Edwin Hubble using the most powerful telescope at the time on Mount Wilson in California. At the time, in the 1920s, scientists believed that the Milky Way galaxy was the totality of the universe. Objects in the night sky like Andromeda that we now know are galaxies were called “nebulae”.

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All navigations reported in Fig. 2 were performed autonomously within 150 s and without intraoperative imaging. Specifically, each navigation was performed according to the pre-determined optimal actuation fields and supervised in real time by intraoperative localization. Therefore, the set of complex navigations performed by the magnetic tentacle was possible without the need for exposure to radiation-based imaging. In all cases, the soft magnetic tentacle is shown to conform by design to the anatomy thanks to its low stiffness, optimal magnetization profile and full-shape control. Compared to a stiff catheter, the non-disruptive navigation achieved by the magnetic tentacle can improve the reliability of registration with pre-operative imaging to enhance both navigation and targeting. Moreover, compared to using multiple catheters with different pre-bent tips, the optimization approach used for the magnetic tentacle design determines a single magnetization profile specific to the patient’s anatomy that can navigate the full range of possible pathways illustrated in Fig. 2. Supplementary Movies S1 and S2 report all the experiments. Supplementary Movie S1 shows the online tracking capabilities of the proposed platform.

In Table 1, we report the results of the localization for four different scenarios. These cases highlight diverse navigations in the left and right bronchi. The error is referred to as the percentage of tentacles outside the anatomy. This was computed by intersecting the shape of the catheter, as predicted by the FBG sensor, and the anatomical mesh grid extracted from the CT scan. The portion of the tentacle within the anatomy was measured by using “inpolyhedron” function in MATLAB. In Supplementary Movie S1, this is highlighted in blue, while the section of the tentacle outside the anatomy is marked in red. The error in Table 1 was computed using the equation.

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